Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia.
School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria, Australia.
PLoS One. 2022 Nov 14;17(11):e0277555. doi: 10.1371/journal.pone.0277555. eCollection 2022.
The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject's EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.
神经系统疾病的诊断是现代医学面临的最大挑战之一，这是目前的一个主要问题。脑电图（EEG）记录通常用于识别各种神经系统疾病。EEG 产生大量多通道时序列数据，神经科医生通过对这些数据进行视觉分析，以识别和理解大脑内部的异常以及它们的传播方式。这是一个耗时、容易出错、主观和耗费精力的过程。此外，最近在 EEG 分类方面的进展主要集中在使用 EEG 数据从健康受试者中分类特定疾病的患者，这在经济上是不可行的，因为它需要多个系统来检查受试者的 EEG 数据是否存在不同的神经障碍。这迫使研究人员推进他们的工作，并创建一个单一的、统一的分类框架，以从 EEG 信号数据中识别各种神经系统疾病。因此，本研究旨在通过开发一种基于机器学习（ML）的数据挖掘技术，从 EEG 数据中对多种异常进行分类，以满足这一要求。在时频光谱图像上使用纹理特征提取器和基于 ML 的分类器来开发分类系统。首先，使用滤波技术从信号中去除噪声和伪影，然后进行归一化以降低计算复杂度。之后，将归一化后的信号分割成小的时间片段，并使用短时傅里叶变换从这些片段生成频谱图图像。然后，使用两个基于直方图的纹理特征提取器分别计算特征，并使用主成分分析从提取的特征中选择显著特征。最后，使用四种不同的基于 ML 的分类器将这些选择的特征分类为不同的疾病类别。所开发的方法在四个实时 EEG 数据集上进行了测试。所得到的结果表明，该方法在分类各种异常类型方面具有潜力，表明它可以用于从脑信号数据中识别各种神经异常。
